COS424/SML302: Fundamentals of Machine Learning
Monday and Wednesday 8:30AM-10:20AM
Friend Center 101
Wednesdays, 2:30PM-3:20PM; Thursdays, 10:00AM-10:50AM; Fridays, 3:30PM-4:20PM
Computer Science 105
Office: Computer Science 322
Hours: Monday 10:00-11:00AM; COS 302
Diana Cai, Jonathan Lu, Guillaume Martinet, Matthew Meyers, Archit Verma, Tianju Xue.
For TA office hours and locations, see Piazza website.
Description, syllabus, and readings
Administrative To Do
Sign up on
Take the seven minute
! We will use these data in our in-class examples.
All course materials, demos, homeworks, and project descriptions will be posted on the
Piazza course website
Python coding and machine learning:
includes many python packages for a large range of machine learning methods and models.
is a simple data analysis tool for working with data in a reproducible way.
Here are some resources for learning and using R if you care to use R in visualization (project code is expected to be in Python):
Download R at the
R Project for Statistical Computing
Start to learn R by reading
Introductory Statistics with R
by Peter Dalgaard (Ch 1-2).
Many people like
Some people use
Emacs Speaks Statistics
for beautiful graphics and figures.
for reproducible R pipelines.
Additional books and reading that you might find useful (Murphy book PDF link is on the Piazza page):
The Hastie et al. book,
Elements of Statistical Learning
can be found
Introduction to Statistical Thought
(an introductory statistical textbook with plenty of R examples, and it's online too)
David J.C. MacKay
Information Theory, Inference, and Learning Algorithms
(PDF available online)
Pattern Recognition and Machine Learning
Daphne Koller & Nir Friedman,
Probabilistic Graphical Models